I am using an ordinal independent variable in an OLS regression as a categorical variable using the factor variable technique in Stata (i.e, i.ordinal). The variable can take on values of the integers from 0 to 9, with 0 being the base category. I am interested in testing if the coefficient of each variable is greater (or less) than that which succeeds it (i.e. _b[1.ordinal] >= _b[2.ordinal], _b[2.ordinal] >= _b[3.ordinal], etc.). I've started with the following pseudocode based on FAQ: One-sided t-tests for coefficients:
foreach i in 1 2 3 5 6 7 8 {
test _b[`i'.ordinal] - _b[`i+'.ordinal] = 0
gen sign_`i'`i+' = sign(_b[`i'.ordinal] - _b[`i+'.ordinal])
display "Ho: i <= i+ p-value = " ttail(r(df_r), sign_`i'`i+'*sqrt(r(F)))
display "Ho: i >= i+ p-value = " 1-ttail(r(df_r), sign_`i'`i+'*sqrt(r(F)))
}
where I want the ```i+' to mean the next value of i in the sequence (so if i is 3 then ``i+' is 5). Is this even possible to do? Of course, if you have any cleaner suggestions to test the coefficients in this manner, please advise.
Note: The model only uses a sub-sample of my dataset for which there are no observations for 4.ordinal, which is why I use foreach instead of forvalues. If you have suggestions for developing a general code that can be used regardless of missing variables, please advise.
There are various ways to do this. Note that there is little obvious point to creating a new variable just to hold one constant. Code not tested.
forval i = 1/8 {
local j = `i' + 1
capture test _b[`i'.ordinal] - _b[`j'.ordinal] = 0
if _rc == 0 {
local sign = sign(_b[`i'.ordinal] - _b[`j'.ordinal])
display "Ho: `i' <= `j' p-value = " ttail(r(df_r), `sign' * sqrt(r(F)))
display "Ho: `i' >= `j' p-value = " 1-ttail(r(df_r), `sign' * sqrt(r(F)))
}
}
The capture should eat errors.
Related
In my Stata data set, the "alternative" variable consists of 4 modes including pier, private, beach and charter.
I want to generate new variable y as follows:
We collapse the model to three alternatives and order the alternatives, with y = 0 if fishing from a pier or beach, y = 1 if fishing from a private boat and y = 2 if fishing from a charter.
I tried to do this by looking at thetas in this website:
stata tips but I can't solve it.
Note: I don't understand from the dataset. And I get error related to type of the variable while generating variable I download the dataset from the website https://www.stata-press.com/data/musr/musr.zip The data name is mus15data
The variables in the dataset is as follows:
Here, "mode" variable is alternatives.
If I understand correctly, this is
gen y = 0 if inlist(1, dbeach, dpier)
* gen y = 0 if dbeach == 1 | dpier == 1
replace y = 1 if dprivate == 1
replace y = 2 if dcharter == 1
Many other solutions are possible. Here is one more.
gen y = cond(inlist(1, dbeach, pier), 0, 2 * (dcharter == 1) + (dprivate == 1))
If all those variables are only ever 0 or 1 (and never missing) some simplifications are possible.
Go only with code you find clear and can explain to others.
I am assuming that pier, beach, private, charter are mutually exclusive. I've not checked with the dataset.
Goal: perform rolling window calculations on panel data in Stata with variables PanelVar, TimeVar, and Var1, where the window can change within a loop over different window sizes.
Problem: no access to SSC for the packages that would take care of this (like rangestat)
I know that
by PanelVar: gen Var1_1 = Var1[_n]
produces a copy of Var1 in Var1_1. So I thought it would make sense to try
by PanelVar: gen Var1SumLag = sum(Var1[(_n-3)/_n])
to produce a rolling window calculation for _n-3 to _n for the whole variable. But it fails to produce the results I want, it just produces zeros.
You could use sum(Var1) - sum(Var1[_n-3]), but I also want to be able to make the rolling window left justified (summing future observations) as well as right justified (summing past observations).
Essentially I would like to replicate Python's ".rolling().agg()" functionality.
In Stata _n is the index of the current observation. The expression (_n - 3) / _n yields -2 when _n is 1 and increases slowly with _n but is always less than 1. As a subscript applied to extract values from observations of a variable it always yields missing values given an extra rule that Stata rounds down expressions so supplied. Hence it reduces to -2, -1 or 0: in each case it yields missing values when given as a subscript. Experiment will show you that given any numeric variable say numvar references to numvar[-2] or numvar[-1] or numvar[0] all yield missing values. Otherwise put, you seem to be hoping that the / yields a set of subscripts that return a sequence you can sum over, but that is a long way from what Stata will do in that context: the / is just interpreted as division. (The running sum of missings is always returned as 0, which is an expression of missings being ignored in that calculation: just as 2 + 3 + . + 4 is returned as 9 so also . + . + . + . is returned as 0.)
A fairly general way to do what you want is to use time series operators, and this is strongly preferable to subscripts as (1) doing the right thing with gaps (2) automatically working for panels too. Thus after a tsset or xtset
L0.numvar + L1.numvar + L2.numvar + L3.numvar
yields the sum of the current value and the three previous and
L0.numvar + F1.numvar + F2.numvar + F3.numvar
yields the sum of the current value and the three next. If any of these terms is missing, the sum will be too; a work-around for that is to return say
cond(missing(L3.numvar), 0, L3.numvar)
More general code will require some kind of loop.
Given a desire to loop over lags (negative) and leads (positive) some code might look like this, given a range of subscripts as local macros i <= j
* example i and j
local i = -3
local j = 0
gen double wanted = 0
forval k = `i'/`j' {
if `k' < 0 {
local k1 = -(`k')
replace wanted = wanted + L`k1'.numvar
}
else replace wanted = wanted + F`k'.numvar
}
Alternatively, use Mata.
EDIT There's a simpler method, to use tssmooth ma to get moving averages and then multiply up by the number of terms.
tssmooth ma wanted1=numvar, w(3 1)
tssmooth ma wanted2=numvar, w(0 1 3)
replace wanted1 = 4 * wanted1
replace wanted2 = 4 * wanted2
Note that in contrast to the method above tssmooth ma uses whatever is available at the beginning and end of each panel. So, the first moving average, the average of the first value and the three previous, is returned as just the first value at the beginning of each panel (when the three previous values are unknown).
In Stata: So far I have
foreach i of varlist v8 - v23 {
gen v_`i' = (`i'[_n-2]*`i'[_n-3] * `i'[_n-4])/3
}
But this isn't doing it.
That averages previous observations, not previous variables.
Your question title is about the average of two columns (meaning variables). Your code looks like an attempt to code the average of three variables. I will run with the second.
Your variables run 8 to 23; so the first average is for position 11 as the average of 8, 9, 10.
forval j = 11/23 {
local 3 = `j' - 3
local 2 = `j' - 2
local 1 = `j' - 1
generate v_`j' = (v`3' + v`2' + v`1') / 3
}
That could be squeezed into fewer lines, but it seems more important to show some structure clearly.
If 8 to 23 represent different times, and in many other situations too, you would be better off after a reshape long.
I want to simulate the item score from total score.
For example, I have generated the total score, which has scores between 5 and 25. I would like to distribute this total score to five items with each having a 5-Likert score.
Then I used a while loop to check the condition in Stata 15. The code takes took too long to finish looping and I do not know whether I have made a mistake.
Perhaps someone would like to suggest another way to simulate the item score from the total score?
My code:
set obs 200
generate id=_n
generate u_i= rnormal(0, 0.5)
generate gr = runiform()>0.5
generate sex = runiform()>0.4
generate age = round(rnormal(65, 10))
expand 5
bysort id: generate time=_n
generate e_ij = rnormal(0, 1.0)
generate run=_n
*Generate Sum score 5-25
generate y = 3.0 + 2.0*gr + 0.2*age -1.2*sex + 0.5*time + u_i + e_ij
summarize y
replace y = round(y)
*Generate each item
forvalues k = 1(1)5 {
generate item`k' = runiform(1, 5)
replace item`k' = round(item`k')
}
egen sum_item=rowtotal(item1 item2 item3 item4 item5)
generate diff = y - sum_item
*Looping check if y=sum_item
forvalues a = 1(1)`=_N' {
quietly gsort -diff
while sum_item!=y[`a'] {
replace sum_item=. if sum_item!=y[_n]
forvalues k = 1(1)5 {
replace item`k' =. if sum_item==.
replace item`k' = runiform(1, 5) if item`k'==.
replace item`k' = round(item`k')
}
replace sum_item= item1 + item2+item3+item4+item5 if sum_item==.
replace diff = y - sum_item
if (sum_item==y[`a']) continue, break
}
}
The expected data that I would like to have:
As you can see, after running the loop I will always get 2-4 cases that the program keep running by generating item score (item1-item5) until the diff variable equals zero.
If I'm understanding correctly, you could loop something like the following (after setting all the items to initial values of 1, since possible values are 1 to 5):
capture generate rand_int = 0
replace rand_int = floor( 5 * runiform() + 1 ) // random int, 1 to 5
capture generate cnd = 0
forvalues k = 1(1)5 {
replace cnd = rand_int == `k' & sum_item < y & item`k' < 6
replace item`k' = item`k' + 1 if cnd
}
replace sum_item = item1+item2+item3+item4+item5
In words, that says is that if sum_item < y, then randomly add 1 to one of the items (as long as that item is not already equal to 5), and then you would keep doing it until sum_item == y for all rows.
So that's going to converge in roughly 20 iterations if the max value of y is 25 and items are from 1 to 5. I say "roughly" because there is a little waste in here when you add 1 to an item that is already equal to 5. You could ad some extra code for that, but I wouldn't bother if this is fast enough. E.g. for high values of item_sum it would be more efficient to start with initial values of 5 and randomly subtract 1 until it converges.
I'm not enough of a statistician to say that's the best or even an adequate way to do it, but intuitively to me it seems OK if you want a fairly uniform distribution of values. If you wanted the modal value to be 4, for example, that's a lot harder and not really a programming question any longer.
I need to program a nearest neighbor algorithm in stata from scratch because my dataset does not allow me to use any of the available solutions (as far as I am concerned).
To be pecise. I have a dataset that is of similar structure to that of the following (original has around 14k observations)
input id value treatment match
1 0.14 0 .
2 0.32 0 .
3 0.465 1 2
4 0.878 1 2
5 0.912 1 2
6 0.001 1 1
end
I want to generate a variable called match (already included in the example above). For each observation with treatment == 1 the variable match should store the id of another observation from within treatment == 0 whose value is closest to value of the considered observation (treatment == 1).
I am new to stata programming, so I am not yet familiar with the syntax. My first shot is the following however it does not produce any changes to the match variable. I am sure this is a novice question but I am hoping for some advice on how to make the code running.
EDIT: I have changed the code slightly and now it seems to work. Do you see any problems that may arise if I run it on a bigger dataset?
set more off
clear all
input id pscore treatment
1 0.14 0
2 0.32 0
3 0.465 1
4 0.878 1
5 0.912 1
6 0.001 1
end
gen match = .
forval i = 1/`= _N' {
if treatment[`i'] == 1 {
local dist 1
forvalues j = 1/`= _N' {
if (treatment[`j'] == 0) {
local current_dist (pscore[`i'] - pscore[`j'])^2
if `dist' > `current_dist' {
local dist `current_dist' // update smallest distance
replace match = id[`j'] in `i' // write match
}
}
}
}
}
Consider some simulated data: 1,000 observations, 200 of them untreated (treat == 0) and the rest treated (treat == 1). Then the code included below will be much more efficient than the originally posted. (Ties, like in your code, are not explicitly handled.)
clear
set more off
*----- example data -----
set obs 1000
set seed 32956
gen id = _n
gen pscore = runiform()
gen treat = cond(_n <= 200, 0, 1)
*----- new method -----
timer clear
timer on 1
// get id of last non-treated and first treated
// (data is sorted by treat and ids are consecutive)
bysort treat (id): gen firsttreat = id[1]
local firstt = first[_N]
local lastnt = `firstt' - 1
// start loop
gen match = .
gen dif = .
quietly forvalues i = `firstt'/`=_N' {
// compute distances
replace dif = (pscore[`i'] - pscore)^2
summarize dif in 1/`lastnt', meanonly
// identify id of minimum-distance observation
replace match = . in 1/`lastnt'
replace match = id in 1/`lastnt' if dif == r(min)
summarize match in 1/`lastnt', meanonly
// save the minimum-distance id
replace match = r(max) in `i'
}
// clean variable and drop
replace match = . in 1/`lastnt'
drop dif firsttreat
timer off 1
tempfile first
save `first'
*----- your method -----
drop match
timer on 2
gen match = .
quietly forval i = 1/`= _N' {
if treat[`i'] == 1 {
local dist 1
forvalues j = 1/`= _N' {
if (treat[`j'] == 0) {
local current_dist (pscore[`i'] - pscore[`j'])^2
if `dist' > `current_dist' {
local dist `current_dist' // update smallest distance
replace match = id[`j'] in `i' // write match
}
}
}
}
}
timer off 2
tempfile second
save `second'
// check for equality of results
cf _all using `first'
// check times
timer list
The results in seconds to finish execution:
. timer list
1: 0.19 / 1 = 0.1930
2: 10.79 / 1 = 10.7900
The difference is huge, specially considering this data set has only 1,000 observations.
An interesting thing to notice is that as the number of non-treated cases increases relative to the number of treated, then the original method improves, but never reaches the levels of efficiency of the new method. As an example, invert the number of cases, so there is now 800 untreated and 200 treated (change data setup to gen treat = cond(_n <= 800, 0, 1)). The result is
. timer list
1: 0.07 / 1 = 0.0720
2: 4.45 / 1 = 4.4470
You can see that the new method also improves and is still much faster. In fact, the relative difference is still the same.
Another way to do this is using joinby or cross. The problem is they temporarily expand (a lot) the size of your data base. In many cases, they are not feasible due to the hard limit Stata has on the number of possible observations (see help limits). You can find an example of joinby here: https://stackoverflow.com/a/19784222/2077064.
Edit
If there's a large number of treated relative to untreated, your code suffers
because you go through the whole first loop many more times (due to the first if).
Furthermore, going through
that whole loop once, implies going through another loop that
has itself two if conditions, _N more times.
The opposite case in which there are few treated observations means that you go through the whole
first loop only in a small number of occasions, speeding up your code substantially.
The reason my code can maintain its efficiency is due to the use of in. This always
offers speed gains over if. Stata will go directly to those observations with no
logical checking needed. Your problem provides an opportunity for that replacement
and it's wise to seize it.
If my code used if where in is in place, the results would be different.
Your code would be faster for the
case in which there's a large number of untreated relative to treated, and again, that
is because in your code there would not be the need to go through the complete loop,
requiring very little work;
the first loop is short-circuited with the first if. For the opposite case,
my code would still dominate.
The key is to "separate" treated from untreated and work on each group using in.